{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#读取数据\n",
    "data = pd.read_json('trump_tweets_2009~2018.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>created_at</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>id_str</th>\n",
       "      <th>in_reply_to_user_id_str</th>\n",
       "      <th>is_retweet</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33659</th>\n",
       "      <td>2018-01-10 04:23:58</td>\n",
       "      <td>60252</td>\n",
       "      <td>950946293077209088</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>13822</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>.@ICEgov HSI agents and ERO officers, on behal...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33660</th>\n",
       "      <td>2018-01-10 03:48:26</td>\n",
       "      <td>51951</td>\n",
       "      <td>950937350003183616</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>11893</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Thank you @GOPLeader Kevin McCarthy! Couldn’t ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33661</th>\n",
       "      <td>2018-01-10 00:16:57</td>\n",
       "      <td>123413</td>\n",
       "      <td>950884128379035648</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>24552</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>As I made very clear today, our country needs ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33662</th>\n",
       "      <td>2018-01-09 23:59:38</td>\n",
       "      <td>0</td>\n",
       "      <td>950879772644659200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>11716</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>RT @Scavino45: “Utilities cutting rates, cite ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33663</th>\n",
       "      <td>2018-01-09 23:07:09</td>\n",
       "      <td>82151</td>\n",
       "      <td>950866561153331200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>19481</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Today, it was my great honor to sign a new Exe...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               created_at  favorite_count              id_str  \\\n",
       "33659 2018-01-10 04:23:58           60252  950946293077209088   \n",
       "33660 2018-01-10 03:48:26           51951  950937350003183616   \n",
       "33661 2018-01-10 00:16:57          123413  950884128379035648   \n",
       "33662 2018-01-09 23:59:38               0  950879772644659200   \n",
       "33663 2018-01-09 23:07:09           82151  950866561153331200   \n",
       "\n",
       "       in_reply_to_user_id_str  is_retweet  retweet_count              source  \\\n",
       "33659                      NaN       False          13822  Twitter for iPhone   \n",
       "33660                      NaN       False          11893  Twitter for iPhone   \n",
       "33661                      NaN       False          24552  Twitter for iPhone   \n",
       "33662                      NaN        True          11716  Twitter for iPhone   \n",
       "33663                      NaN       False          19481  Twitter for iPhone   \n",
       "\n",
       "                                                    text  \n",
       "33659  .@ICEgov HSI agents and ERO officers, on behal...  \n",
       "33660  Thank you @GOPLeader Kevin McCarthy! Couldn’t ...  \n",
       "33661  As I made very clear today, our country needs ...  \n",
       "33662  RT @Scavino45: “Utilities cutting rates, cite ...  \n",
       "33663  Today, it was my great honor to sign a new Exe...  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#预览部分数据\n",
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The lenght's average in tweets: 112.30\n"
     ]
    }
   ],
   "source": [
    "#平均长度\n",
    "mean_length = np.mean([len(i) for i in data.text])\n",
    "print(\"The lenght's average in tweets: %.2f\" % mean_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The tweets with more like:\n",
      "[ 'Such a beautiful and important evening! The forgotten man and woman will never be forgotten again. We will all come together as never before']\n",
      "Numbers of like: 633253\n",
      "\n",
      "The tweets with more reweet:\n",
      "['#FraudNewsCNN #FNN https://t.co/WYUnHjjUjg']\n",
      "Numbers of reweet: 369530\n"
     ]
    }
   ],
   "source": [
    "#最高赞与最高转发\n",
    "max_fav_num = data.favorite_count.max()\n",
    "max_fav_text = data.text[data.favorite_count == max_fav_num]\n",
    "\n",
    "max_ret_num = data.retweet_count.max()\n",
    "max_ret_text = data.text[data.retweet_count == max_ret_num]\n",
    "\n",
    "print('The tweets with more like:\\n{}'.format(max_fav_text.values))\n",
    "print('Numbers of like: {}'.format(max_fav_num))\n",
    "print()\n",
    "print('The tweets with more reweet:\\n{}'.format(max_ret_text.values))\n",
    "print('Numbers of reweet: {}'.format(max_ret_num))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18702d55390>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uRXb77beXJLIC6NWrF1u3bqV379643W7at2/PeeedB0BCQgJffvklDz/8MF26dKFr164l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\nkF9UHKZmYwlzYyfBeBNlHWq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125qsMkmcQgW6PrfETS6zhdFpzoce0RrgnsaF7iVlC1t1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\np07HSG+FC+UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x187035d6550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#点赞量与转发量分析\n",
    "%matplotlib inline\n",
    "\n",
    "fav_tweets = pd.Series(data = data.favorite_count.values,index = data.created_at)\n",
    "ret_tweets = pd.Series(data = data.retweet_count.values,index = data.created_at)\n",
    "\n",
    "fav_tweets.plot(figsize=(16,4), label=\"favorite_count\", legend=True)\n",
    "ret_tweets.plot(figsize=(16,4), label=\"retweet_count\", legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
